
KronRLS [1]} applies a kernel least square method on the similarity matrix of drug and protein features.

LR [2]}, [3]} applies a logistic regression model on the concatenated drug and protein feature vectors. We experiment on all the combinations for ECFP4 [3]} & PubChem [5]} for drugs and protein sequence composition descriptor (PSC) [2]} & Composition-Transition-Distribution (CTD) [7]} for proteins. We find ECFP4 for drugs and PSC for protein has the highest performance.

DNN uses a three layer deep neural network with hidden size 1,024 on top of the ECFP4 and PSC concatenated vector.

DeepDTI [8]} models DTI using Deep Belief Network [9]}, which is a stack of Restricted Boltzmann Machines [10]}. It uses the concatenation of ECFP2, ECFP4, ECFP6 as the drug feature and uses PSC for protein features. We optimize the hyper-parameters described from the paper based on validation set performance.

DeepDTA [11]} applies CNN on both raw SMILES string and protein sequence to extract local residue patterns. The task is to predict binding affinity values. We add a Sigmoid activation function in the end to change it to a binary classification problem and we conduct hyper-parameter search to ensure fairness.

GNN-CPI [12]} uses graph neural network to encode drugs and use CNN to encode proteins. The latent vectors are then concatenated into a neural network for compound-protein interaction prediction. We follow the same hyper-parameter setting described in the paper.

DeepConv-DTI [13]} uses CNN and global max pooling layer to extract various length local pattern in protein sequence and applies fully connected layer on drug fingerprint ECFP4. It conducts extensive experiment on different datasets and is the state-of-the-art model in DTI binary prediction task. We follow the same hyper-parameter setting described in the paper.

